tushare
V0.2.0
TuShare
Tushare Pro version has been released, please visit the new official website to learn and query the data interface! https://tushare.pro
TuShare is a tool that implements the data collection , cleaning and processing to data storage process of financial data such as stocks/futures. It meets the data acquisition needs of financial quantitative analysts and people who study data analysis. It is characterized by wide data coverage. The interface call is simple and the response is fast.
Welcome to scan TuShare’s WeChat public account “Digging Rabbit” for more resources and information to share with you. In addition, since the tushare official website is being redesigned and developed, the latest interface usage documents will be released on the DiDi Rabbit official account, so please scan the QR code to follow, thank you!
QQ communication group:
python 2.x/3.x
pandas
pip install tushare --upgrade
Example 1. Obtain historical transaction data of individual stocks (including moving average data):
import tushare as ts
ts.get_hist_data('600848') #一次性获取全部数据
另外，参考get_k_data函数
The results show:
Date, opening price, highest price, closing price, lowest price, trading volume, price change, increase or decrease, 5-day average price, 10-day average price, 20-day average price, 5-day average volume, 10-day average volume, 20 Average daily volume, turnover rate
open high close low volume p_change ma5
date
2012-01-11 6.880 7.380 7.060 6.880 14129.96 2.62 7.060
2012-01-12 7.050 7.100 6.980 6.900 7895.19 -1.13 7.020
2012-01-13 6.950 7.000 6.700 6.690 6611.87 -4.01 6.913
2012-01-16 6.680 6.750 6.510 6.480 2941.63 -2.84 6.813
2012-01-17 6.660 6.880 6.860 6.460 8642.57 5.38 6.822
2012-01-18 7.000 7.300 6.890 6.880 13075.40 0.44 6.788
2012-01-19 6.690 6.950 6.890 6.680 6117.32 0.00 6.770
2012-01-20 6.870 7.080 7.010 6.870 6813.09 1.74 6.832
ma10 ma20 v_ma5 v_ma10 v_ma20 turnover
date
2012-01-11 7.060 7.060 14129.96 14129.96 14129.96 0.48
2012-01-12 7.020 7.020 11012.58 11012.58 11012.58 0.27
2012-01-13 6.913 6.913 9545.67 9545.67 9545.67 0.23
2012-01-16 6.813 6.813 7894.66 7894.66 7894.66 0.10
2012-01-17 6.822 6.822 8044.24 8044.24 8044.24 0.30
2012-01-18 6.833 6.833 7833.33 8882.77 8882.77 0.45
2012-01-19 6.841 6.841 7477.76 8487.71 8487.71 0.21
2012-01-20 6.863 6.863 7518.00 8278.38 8278.38 0.23
Set the time of historical data:
ts.get_hist_data('600848',start='2015-01-05',end='2015-01-09')
open high close low volume p_change ma5 ma10
date
2015-01-05 11.160 11.390 11.260 10.890 46383.57 1.26 11.156 11.212
2015-01-06 11.130 11.660 11.610 11.030 59199.93 3.11 11.182 11.155
2015-01-07 11.580 11.990 11.920 11.480 86681.38 2.67 11.366 11.251
2015-01-08 11.700 11.920 11.670 11.640 56845.71 -2.10 11.516 11.349
2015-01-09 11.680 11.710 11.230 11.190 44851.56 -3.77 11.538 11.363
ma20 v_ma5 v_ma10 v_ma20 turnover
date
2015-01-05 11.198 58648.75 68429.87 97141.81 1.59
2015-01-06 11.382 54854.38 63401.05 98686.98 2.03
2015-01-07 11.543 55049.74 61628.07 103010.58 2.97
2015-01-08 11.647 57268.99 61376.00 105823.50 1.95
2015-01-09 11.682 58792.43 60665.93 107924.27 1.54
Restoration historical data Obtain historical re-rights data, which is divided into pre-right re-rights and post-right re-rights data. The interface provides all historical data since the stock was listed. The default is pre-right re-rights. If the start and end dates are not set, the re-righting data of the past year will be returned. From a performance perspective, it is recommended to set the start and end dates, and it is best not to exceed one year. After obtaining the data, please update it locally in a timely manner. storage.
ts.get_h_data('002337') #前复权
ts.get_h_data('002337',autype='hfq') #后复权
ts.get_h_data('002337',autype=None) #不复权
ts.get_h_data('002337',start='2015-01-01',end='2015-03-16') #两个日期之间的前复权数据
Example 2. Obtain the transaction data of all stocks on the latest trading day at one time (the result display speed depends on the network speed)
ts.get_today_all()
The results show:
Code, name, price increase or decrease, current price, opening price, highest price, lowest price, last closing price, trading volume, turnover rate
code name changepercent trade open high low settlement
0 002738 中矿资源 10.023 19.32 19.32 19.32 19.32 17.56
1 300410 正业科技 10.022 25.03 25.03 25.03 25.03 22.75
2 002736 国信证券 10.013 16.37 16.37 16.37 16.37 14.88
3 300412 迦南科技 10.010 31.54 31.54 31.54 31.54 28.67
4 300411 金盾股份 10.007 29.68 29.68 29.68 29.68 26.98
5 603636 南威软件 10.006 38.15 38.15 38.15 38.15 34.68
6 002664 信质电机 10.004 30.68 29.00 30.68 28.30 27.89
7 300367 东方网力 10.004 86.76 78.00 86.76 77.87 78.87
8 601299 中国北车 10.000 11.44 11.44 11.44 11.29 10.40
9 601880 大连港 10.000 5.72 5.34 5.72 5.22 5.20
10 000856 冀东装备 10.000 8.91 8.18 8.91 8.18 8.10
volume turnoverratio
0 375100 1.25033
1 85800 0.57200
2 1058925 0.08824
3 69400 0.51791
4 252220 1.26110
5 1374630 5.49852
6 6448748 9.32700
7 2025030 6.88669
8 433453523 4.28056
9 323469835 9.61735
10 25768152 19.51090
Example 3. Obtain historical data
import tushare as ts
df = ts.get_tick_data('600848',date='2014-01-09')
df.head(10)
The results show:
Transaction time, transaction price, price change, transaction lot, transaction amount (yuan), transaction type
Out[3]:
time price change volume amount type
0 15:00:00 6.05 -- 8 4840 卖盘
1 14:59:55 6.05 -- 50 30250 卖盘
2 14:59:35 6.05 -- 20 12100 卖盘
3 14:59:30 6.05 -0.01 165 99825 卖盘
4 14:59:20 6.06 0.01 4 2424 买盘
5 14:59:05 6.05 -0.01 2 1210 卖盘
6 14:58:55 6.06 -- 4 2424 买盘
7 14:58:45 6.06 -- 2 1212 买盘
8 14:58:35 6.06 0.01 2 1212 买盘
9 14:58:25 6.05 -0.01 20 12100 卖盘
10 14:58:05 6.06 -- 5 3030 买盘
Example 4. Get real-time transaction data (Realtime Quotes Data)
df = ts.get_realtime_quotes('000581') #Single stock symbol
df[['code','name','price','bid','ask','volume','amount','time']]
The results show:
Name, opening price, yesterday's price, current price, highest, lowest, buying price, selling price, trading volume, trading amount... more in docs
code name price bid ask volume amount time
0 000581 威孚高科 31.15 31.14 31.15 8183020 253494991.16 11:30:36
Request multiple stock methods (preferably no more than 30 at a time):
ts.get_realtime_quotes(['600848','000980','000981']) #symbols from a list
ts.get_realtime_quotes(df['code'].tail(10)) #from a Series
https://tushare.pro
http://tushare.org/
Added 'Dragon and Tiger List' module
Modify the get_h_data data type to float
Modify the open column missed by the get_index interface
Merge bug fixes submitted on GitHub
